Predictive biomarkers of pancreatic cancer metastasis: A comprehensive review

IF 2.9 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2025-03-01 Epub Date: 2025-02-04 DOI:10.1016/j.cca.2025.120176
Mengting Chen , Hongsen Liu , Yufei Xiao , Ruijin Liang , Hong Xu , Bo Hong , Yun Qian
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Abstract

This review provides a comprehensive overview of predictive biomarkers associated with metastasis in pancreatic cancer (PC), one of the most aggressive malignancies characterized by late-stage diagnosis and poor prognosis. Metastasis, particularly to the liver, lungs, and lymph nodes, significantly worsens patient outcomes by compromising organ function and promoting disease progression. Reliable biomarkers for predicting and detecting metastasis at early stages are critical for improving survival rates and guiding personalized therapies. This paper highlights both general and specific biomarkers, including genetic mutations, protein expression changes, and carbohydrate tumor markers such as CA19-9. Immunological factors, including PD-L1, inflammatory cytokines, and chemokines, further influence the metastatic process within the tumor microenvironment (TME). Specific biomarkers play pivotal roles in promoting metastasis through mechanisms such as epithelial-to-mesenchymal transition (EMT), tumor microenvironment remodeling, and immune evasion. Emerging markers such as circulating tumor cells (CTCs) and volatile organic compounds (VOCs) offer promising non-invasive tools for metastasis detection and monitoring. This review not only consolidates existing knowledge but also highlights the mechanisms through which specific biomarkers facilitate metastasis. Despite recent progress, challenges such as biomarker standardization, technical variability, and clinical validation remain, and addressing these hurdles is essential for integrating predictive biomarkers into clinical practice. Ultimately, this review contributes to advancing early detection strategies, personalized treatment options, and improved prognosis for PC patients.
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胰腺癌转移的预测性生物标志物:综述
胰腺癌(PC)是最具侵袭性的恶性肿瘤之一,其特征是晚期诊断和预后差。转移,特别是转移到肝、肺和淋巴结,通过损害器官功能和促进疾病进展而显著恶化患者的预后。预测和检测早期转移的可靠生物标志物对于提高生存率和指导个性化治疗至关重要。本文重点介绍了一般和特定的生物标志物,包括基因突变、蛋白质表达变化和碳水化合物肿瘤标志物,如CA19-9。免疫因素,包括PD-L1、炎症细胞因子和趋化因子,进一步影响肿瘤微环境(TME)内的转移过程。特异性生物标志物通过上皮-间质转化(epithelial-to-mesenchymal transition, EMT)、肿瘤微环境重塑和免疫逃避等机制在促进肿瘤转移中发挥关键作用。循环肿瘤细胞(CTCs)和挥发性有机化合物(VOCs)等新兴标志物为转移检测和监测提供了有前途的非侵入性工具。这篇综述不仅巩固了现有的知识,而且强调了特定生物标志物促进转移的机制。尽管最近取得了进展,但诸如生物标志物标准化、技术可变性和临床验证等挑战仍然存在,解决这些障碍对于将预测性生物标志物整合到临床实践中至关重要。最终,本综述有助于推进PC患者的早期发现策略、个性化治疗选择和改善预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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